Infino vs the alternatives

There is no single best one. The right engine depends on whether you need keyword search next to vectors, SQL over the results, an open storage format, and a server or a library. (how to choose)

• keyword, vector and SQL over the same data, kept as Parquet: Infino
• vectors next to application data already in Postgres: pgvector
• dedicated vector serving at a high sustained query rate: Qdrant or Pinecone

An engine that keeps the index in object storage instead of on replicated block storage, so retained data stops setting the cluster size. (cost-efficient alternatives)

• infino: one searchable copy as Parquet in object storage, with a supported Query DSL subset
• opensearch: the same cluster model under Apache-2.0
• postgres or clickhouse: text search inside a database you already run

The ones that store vectors at object-storage rates and size compute to the queries rather than to the corpus. (cost-efficient alternatives)

• infino: embeddings in Parquet on object storage, IVF with a full-precision rerank, a local cache for the hot part
• serverless vector services: usage pricing that suits uneven traffic
• dedicated vector servers: always on and sized to the index

Extensions keep search next to your data but load the database you scale. A dedicated engine scales search apart but adds a second copy and a sync loop. Infino is a search engine whose results are SQL relations over Parquet. (the trade-offs)

Yes. Infino is a full-text, vector, and hybrid search engine over Parquet, with BM25 and reciprocal-rank fusion built into the query path.

• full-text, vector, and hybrid search run over the same rows and snapshot
• the columns and retrieval indexes live together in standard Parquet
• search results are SQL relations that can be filtered, joined, and aggregated

Yes. Infino provides vector search over Parquet and combines it with BM25, reciprocal-rank fusion, and SQL in the same engine.

• HNSW when the working set is pinned in RAM; OPANN + Sq16 when it lives on object storage
• keyword and vector run in one pass and are fused by RRF inside the query
• the embeddings stay in the Parquet file

It can replace the read path for search, hybrid search, and selected SQL. Snowflake and Databricks retain their broader data-platform ecosystem, while DuckDB remains a strong fit for local analytical work. (vs snowflake / databricks)

• broad scans, large aggregations, BI, governance, notebooks, sharing, and pipelines generally remain on the existing platform
• Infino can write open Parquet and serve as the indexed reader for selective retrieval
• manifest and embedded-index pruning can reduce the bytes decoded before DataFusion runs the remaining SQL plan

Infino runs as an embedded engine or a managed service and reads Parquet from object storage or local block storage. Storage and compute scale apart.

• data lives as standard Parquet on object or block storage, with the search indexes inside the files
• on object storage, hot byte ranges stay in a local RAM and NVMe cache; nothing in that cache is the durable copy
• compute runs over those files and scales independently from corpus growth

You write with append, update, and delete. Each write publishes atomically as a commit, so a query sees the whole batch or none of it.

• a commit writes new Parquet files plus a new manifest, then swaps the current-manifest pointer in one atomic step
• existing files are never mutated: updates and deletes publish a new snapshot over different files
• concurrent writers are guarded, so a writer on a stale manifest retries instead of clobbering another commit
• read freshness follows the table’s consistency policy on every query; indexing is part of the write path

Whether one engine runs keyword and vector search and ranks the results together.

• infino: BM25 + vector, fused with RRF in one query
• elasticsearch: BM25 + kNN + RRF
• opensearch: BM25 + k-NN + search pipelines
• clickhouse: native token filtering plus vector search; application-defined fusion
• postgres: native text ranking plus pgvector; BM25 extension and application-defined rank fusion
• qdrant: dense and sparse retrieval; you set quantization and HNSW
• pinecone: dense and sparse retrieval; managed scoring and hybrid weighting
• snowflake, databricks: warehouse SQL or a managed search product on a separate retrieval path

Whether you can run SQL over the same rows the search indexes cover.

• infino: SQL through DataFusion; search runs as table functions inside the query
• clickhouse: full SQL in its own dialect
• postgres: full Postgres SQL
• elasticsearch: partial, ES|QL and a SQL plugin
• opensearch: partial, SQL plugin and PPL
• snowflake, databricks: full warehouse SQL
• qdrant, pinecone: retrieval APIs

Whether the stored data is Apache Parquet, the format your warehouse and your notebooks already read.

• infino: standard Parquet with the indexes inside it
• clickhouse: reads and writes Parquet, but its native tables are MergeTree
• postgres: Postgres tables
• databricks: Delta / Parquet in your bucket
• snowflake: reads Parquet and Iceberg; native tables are Snowflake
• elasticsearch, opensearch, qdrant, pinecone: engine-private formats

Whether your data stays readable after the engine stops running, without requiring that engine.

• infino: independent Parquet readers can open the columns; the embedded search indexes are Infino-specific
• postgres: anything that speaks Postgres can read the tables
• clickhouse: MergeTree is documented, but read through ClickHouse
• databricks: Delta is an open table format; read through Databricks or any Delta reader
• snowflake: exportable; native storage is Snowflake
• elasticsearch, opensearch, qdrant, pinecone: engine-owned storage formats

Whether the engine you would run yourself is under an open-source license.

• infino: Apache-2
• elasticsearch: AGPL / ELv2, so partial
• opensearch: Apache-2
• clickhouse: Apache-2
• qdrant: Apache-2
• pinecone: closed source, hosted only
• snowflake: closed source, hosted only
• databricks: Spark and Delta are open; the platform is closed
• postgres: PostgreSQL license

A rough five-point comparison of the deployment types modeled here. Corpus size, traffic, availability, region, and discounts can change the order.

• infino, $: retention is billed at object-storage rates, and compute scales apart from it
• clickhouse, $$$: nodes sized for the working set, though object-storage tiers cost less
• postgres, $$$: text, vector, and application workloads scale on the same database
• qdrant, $$$$: always-on servers sized to the index
• pinecone, $$$$: usage-priced serverless; Dedicated Read Nodes at sustained QPS
• elasticsearch, $$$$$: the modeled hot-tier deployment is a replicated cluster; cold tiers differ
• opensearch, $$$$$: Multi-AZ with Standby; UltraWarm and cold tiers differ
• snowflake, $$$$: warehouse compute and storage; hybrid adds Cortex Search
• databricks, $$$$: SQL warehouse DBUs plus the lake in your bucket
• the crossover depends on the workload: constant heavy query traffic narrows the gap

This short list covers the alternatives that appear most often in Infino evaluations. Validate the result against your workload and vendor quotes.